Evaluating Named Entity Recognition and Disambiguation in News and Tweets

Named entity recognition and disambiguation are important for information extraction and populating knowledge bases. Detecting and classifying named entities has traditionally been taken on by the natural language processing community, whilst linking of entities to external resources, such as DBpedia and GeoNames, has been the domain of the Semantic Web community. As these tasks are treated in different communities, it is difficult to assess the performance of these tasks combined.

We present results on an evaluation of the NERD-ML approach on newswire and tweets for both Named Entity Recognition and Named Entity Disambiguation.

5.
NERD-ML
•
The aim of NERD-ML is to combine the
knowledge of the different extractors into a better
named entity recogniser
•
Uses NERD predictions, Stanford NER & extra
features
•
Naive Bayes, k-NN, SMO